Benjamin Grewe: Catalogue data in Spring Semester 2020
|Prof. Dr. Benjamin Grewe
|Systems and Circuits Neuroinformatics
Neuroinformatik u. Neuronale Syst.
ETH Zürich, Y55 G 28
|+41 44 635 30 91
|Information Technology and Electrical Engineering
|Assistant Professor (Tenure Track)
|2V + 1U + 1A
|R. Hahnloser, M. F. Yanik, B. Grewe
|This course introduces principles of information processing in neural systems. It covers basic neuroscience for engineering students, experiment techniques used in animal research and methods for inferring neural mechanisms. Students learn about neural information processing and basic principles of natural intelligence and their impact on artificially intelligent systems.
|This course introduces
- Basic neurophysiology and mathematical descriptions of neurons
- Methods for dissecting animal behavior
- Neural recordings in intact nervous systems and information decoding principles
- Methods for manipulating the state and activity in selective neuron types
- Neuromodulatory systems and their computational roles
- Reward circuits and reinforcement learning
- Imaging methods for reconstructing the synaptic networks among neurons
- Birdsong and language
- Neurobiological principles for machine learning.
|From active membranes to propagation of action potentials. From synaptic physiology to synaptic learning rules. From receptive fields to neural population decoding. From fluorescence imaging to connectomics. Methods for reading and manipulation neural ensembles. From classical conditioning to reinforcement learning. From the visual system to deep convolutional networks. Brain architectures for learning and memory. From birdsong to computational linguistics.
|Prerequisites / Notice
|Before taking this course, students are encouraged to complete "Bioelectronics and Biosensors" (227-0393-10L).
As part of the exercises for this class, students are expected to complete a programming or literature review project to be defined at the beginning of the semester.